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Author Pattanadech, Norasage ♦ Nimsanong, Phethai
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Partial discharges ♦ Training ♦ Noise ♦ Accuracy ♦ Discharges (electric) ♦ Training data ♦ Testing ♦ electromagnetic wave ♦ statistical classification ♦ principal component analysis ♦ k-nearest neighbors ♦ partial discharge pattern
Abstract The aim of this paper is to describe the effect of training methods on the accuracy of PCA-KNN partial discharge (PD) classification model. This model used principal component analysis (PCA) combined with k-nearest neighbor (KNN) model, so called, PCA-KNN PD classification model for PD pattern classification. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were experimented in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 PD experiments in total were performed. The original independent variables for the classification model, skewness and kurtosis of each period of the captured signals, were calculated. To study the effect of training methods: two patterns for data training, odd/even and block training methods were investigated. In case of the block training method, the effect of training data number can be examined as well. Besides, noise signals were generated with the computer program and trained into the PD classification models. The peak of noise signal was set up at 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise — PD signal. Then, the mixed noise — PD signals were used to evaluate the performance of the PCA-KNN PD classification model. It was found that the block data training method provided the higher accuracy PD classification compared with the odd/event data training method. The block training method with 80% training data/20% testing data gave the highest accuracy (95% correction) for PD classification without noise signal. However, this training technique provided the lowest accuracy (56.25% correction) for PD classification with the mixed noise-PD signals.
Description Author affiliation: Electrical Engineering Dept, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang Bangkok, Thailand (Pattanadech, Norasage) || Power System Operation and Control Section 2, Power System Control Dept, Metropolitan Electricity Authority, Bangkok, Thailand (Nimsanong, Phethai)
ISBN 9781479940769
ISSN 21593450
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-10-22
Publisher Place Thailand
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479940752
Size (in Bytes) 316.85 kB
Page Count 5
Starting Page 1
Ending Page 5

Source: IEEE Xplore Digital Library